Microbiota tryptophan metabolism and the biosynthesis of indole derivatives play an important role in homeostasis and pathogenesis in the human body and can be affected by the gut microbiota. However, studies on the interplay between gut microbiota and tryptophan metabolites in patients undergoing dialysis are lacking. This study aimed to identify the gut microbiota, the indole pathway in tryptophan metabolism, and significant functional differences in ESRD patients with regular hemodialysis. We performed the shotgun metagenome sequencing of stool samples from 85 hemodialysis patients. Using the linear discriminant analysis effect size (LEfSe), we examined the composition of the gut microbiota and metabolic features across varying concentrations of tryptophan and indole metabolites. Higher tryptophan levels promoted tyrosine degradation I and pectin degradation I metabolic modules; lower tryptophan levels were associated with glutamate degradation I, fructose degradation, and valine degradation modules. Higher 3-indoxyl sulfate concentrations were characterized by alanine degradation I, anaerobic fatty acid beta-oxidation, sulfate reduction, and acetyl-CoA to crotonyl-CoA. Contrarily, lower 3-indoxyl sulfate levels were related to propionate production III, arabinoxylan degradation, the Entner–Doudoroff pathway, and glutamate degradation II. The present study provides a better understanding of the interaction between tryptophan, indole metabolites, and the gut microbiota as well as their gut metabolic modules in ESRD patients with regular hemodialysis.

In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytics to enhance prediction accuracy, including generalized regression to identify factors that contributed to the differences between the two measures. The mobile app underestimated most macro- and micro-nutrients compared to FFQ (ranges: −5% for total calories, −19% for cobalamin, −33% for vitamin E). The average correlations between the two measures were 0.87 for macro-nutrients and 0.84 for micro-nutrients. Factors that contributed to the differences between the two measures using total calories as an example, included caloric range (1000–2000 versus others), carbohydrate, and protein; for cobalamin, included caloric range, protein, and Chinese diet. Future studies are needed to validate actual intakes and reporting of various diets, and to examine the accuracy of mobile App. Thus, a mobile app can be used to support personalized nutrition in the mHealth era, considering adjustments with sources that could contribute to the inaccurate estimates of nutrients.

Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.

The purpose of this updated meta-analysis was to investigate the effect of nitric oxide synthase-3 (NOS3) G894T polymorphisms, air pollution and their interaction on ischemic heart disease (IHD) risk across populations worldwide. Recursive partition trees, nonlinear association curve fit and geographic information system maps were incorporated to verify results of conventional pooled analyses for sources of heterogeneity. Results from 61 studies (16,219 cases, 12,222 controls) revealed a significant increased relative risk (RR) of IHD associated with NOS3 894 polymorphisms TT (RR = 1.44) and GT (RR = 1.37). Subgroup analysis revealed that the TT polymorphism genotype had significantly increased risk of IHD in Caucasian, East Asian, South Asian, and Middle Eastern populations (all p < 0.05). It is important to point out that many countries demonstrated an average risk of greater than two, which identifies the NOS3 894 TT polymorphism as a potential causal factor and biological marker of IHD, based on criteria for strong evidence used in international consensus panels. These 10 countries include Ukraine, the United Kingdom, Brazil, Chile, Japan, South Korea, India, Iran, Egypt and Morocco. For these countries with elevated risk (RR > 2) from the NOS3 894 TT polymorphism, meta-predictive analysis demonstrated an increasing trend in air pollution association with increased NOS3 894 polymorphisms. Further studies are needed to explore the complexity of the associations among NOS3 gene polymorphisms per population stratifications within countries, detailed air pollution data for added specificity for geographic location across time, and disease risk.

Predictors of healthy eating parameters, including the Healthy Eating Index (HEI), Glycemic Index (GI), and Glycemic Load (GL), were examined using various modern diets (n = 131) in preparation for personalized nutrition in the e-health era. Using Nutrition Data Systems for Research computerized software and artificial intelligence machine-learning-based predictive validation analyses, we included domains of HEI, caloric source, and various diets as the potentially modifiable factors. HEI predictors included whole fruits and whole grains, and empty calories. Carbohydrates were the common predictor for both GI and GL, with total fruits and Mexican diets being additional predictors for GI. The median amount of carbohydrates to reach an acceptable GL < 20 was predicted as 33.95 g per meal (median: 3.59 meals daily) with a regression coefficient of 37.33 across all daily diets. Diets with greater carbohydrates and more meals needed to reach acceptable GL < 20 included smoothies, convenient diets, and liquids. Mexican diets were the common predictor for GI and carbohydrates per meal to reach acceptable GL < 20; with smoothies (12.04), high-school (5.75), fast-food (4.48), Korean (4.30), Chinese (3.93), and liquid diets (3.71) presenting a higher median number of meals. These findings could be used to manage diets for various populations in the precision-based e-health era.

Patients with chronic kidney disease (CKD) and hemodialysis (HD) often experience gut microbiota dysbiosis, which may influence tryptophan metabolism through the kynurenine pathway. We aimed to investigate the specific gut microbiota signatures associated with metabolites involved in the kynurenine pathway in HD patients. In this cross-sectional study, we analyzed serum metabolite concentrations and fecal shotgun metagenomic profiles from 85 patients receiving maintenance hemodialysis at Kaohsiung Medical University Hospital. Fecal samples were analyzed using shotgun metagenomic sequencing to characterize microbiome composition and functional pathways. We measured kynurenine pathway metabolites (kynurenine, kynurenic acid, nicotinuric acid, and trigonelline) by liquid chromatography tandem mass spectrometry and analyzed their associations with microbial species abundance and genomic metabolic modules (GMM). Several bacterial species displayed associations with kynurenine pathway metabolites. Ruminococcus gnavus and Eubacterium sp. CAG:180 demonstrated correlations with kynurenine and kynurenic acid levels, respectively; however, these associations did not persist after adjustment. Distinct levels of metabolites corresponded to distinct patterns of genomic metabolic modules. High kynurenine levels correlated with increased pathways for pectin and arginine degradation. Elevated nicotinuric acid levels were associated with enhanced pathways for anaerobic fatty acid beta-oxidation and propionate production. Trigonelline levels correlated with homocysteine metabolism and various amino acid degradation pathways. Specific microbial taxa and predicted functional modules were associated with circulating kynurenine- and niacin-related metabolites. These cross-sectional findings are hypothesis-generating and do not establish directionality, causality, or therapeutic efficacy. Interpretation is limited by the modest sample size and the absence of a non-hemodialysis control group.